The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Combining fuzzy information from multiple systems (extended abstract)
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Nearest neighbor searching and applications
Nearest neighbor searching and applications
Fast parallel similarity search in multimedia databases
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Parallel processing of nearest neighbor queries in declustered spatial data
GIS '96 Proceedings of the 4th ACM international workshop on Advances in geographic information systems
Similarity query processing using disk arrays
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Enhanced nearest neighbour search on the R-tree
ACM SIGMOD Record
Distance browsing in spatial databases
ACM Transactions on Database Systems (TODS)
Distributed Processing of Similarity Queries
Distributed and Parallel Databases
PDIS '93 Proceedings of the second international conference on Parallel and distributed information systems
Declustering Spatial Databases on a Multi-Computer Architecture
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Cyclic Allocation of Two-Dimensional Data
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Fast Nearest Neighbor Search in High-Dimensional Space
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Indexing the Distance: An Efficient Method to KNN Processing
Proceedings of the 27th International Conference on Very Large Data Bases
Efficient k Nearest Neighbor Queries on Remote Spatial Databases Using Range Estimation
SSDBM '02 Proceedings of the 14th International Conference on Scientific and Statistical Database Management
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
A Model-Based, Open Architecture for Mobile, Spatially Aware Applications
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
A General Multidimensional Data Allocation Method for Multicomputer Database Systems
DEXA '97 Proceedings of the 8th International Conference on Database and Expert Systems Applications
Concentric Hyperspaces and Disk Allocation for Fast Parallel Range Searching
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Distributed computation of the knn graph for large high-dimensional point sets
Journal of Parallel and Distributed Computing
Integrated k-NN query processing based on geospatial data services
GCC'05 Proceedings of the 4th international conference on Grid and Cooperative Computing
Hi-index | 0.00 |
We propose a family of algorithms for processing nearest neighbor (NN) queries in an integration middleware that provides federated access to numerous loosely coupled, autonomous data sources connected through the internet. Previous approaches for parallel and distributed NN queries considered all data sources as relevant, or determined the relevant ones in a single step by exploiting additional knowledge on object counts per data source. We propose a different approach that does not require such detailed statistics about the distribution of the data. It iteratively enlarges and shrinks the set of relevant data sources. Our experiments show that this yields considerable performance benefits with regard to both response time and effort. Additionally, we propose to use only moderate parallelism instead of querying all relevant data sources at the same time. This allows us to trade a slightly increased response time for a lot less effort, hence maximizing the cost profit ratio, as we show in our experiments. Thus, the proposed algorithms clearly extend the set of NN algorithms known so far.